Scientists can now model NZ's winter illnesses in real time

Publicly released:
Australia; New Zealand
COVID-19, RSV, and Flu 3D renditions of three respiratory viruses: COVID-19 (left), RSV (center; oblong shape), and flu (right). Credit: National Institute of Allergy and Infectious Diseases via Unsplash
COVID-19, RSV, and Flu 3D renditions of three respiratory viruses: COVID-19 (left), RSV (center; oblong shape), and flu (right). Credit: National Institute of Allergy and Infectious Diseases via Unsplash

For the first time, in winter 2025, experts looked at rates of respiratory illness in NZ in real time and modelled how they might change over the coming weeks. In a new paper, NZ and Australian scientists explain how they combined national COVID data with RSV and flu numbers from hospital surveillance, to study trends and provide weekly forecasts of cases and hospitalisations as part of a trans-Tasman programme. The models did reasonably well, the authors say - and as they further update the models and get more years of data on RSV and flu, they'll be able to build more detailed forecasts to help healthcare systems plan ahead.

News release

From: The Royal Society

Every winter, respiratory diseases cause significant sickness and put pressure on healthcare systems. Having an up-to-date picture of epidemic trends can help public health systems to plan and respond. In the 2025 winter season, we used a combination of real-time surveillance data and mathematical models to provide weekly intelligence reports on respiratory illness to public health agencies in New Zealand. Our models included the three major respiratory diseases – Covid-19, influenza and RSV – and included analysis of the latest trends and forecasts for the following 4 weeks.

Expert Reaction

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Professor Michael Plank, School of Mathematics and Statistics, University of Canterbury, and lead author of this paper

"Every winter, respiratory diseases cause significant sickness and put pressure on healthcare systems, but the exact timing and mix of viruses can vary a lot from one year to another. Having an up-to-date picture of epidemic trends can help public health systems plan for periods of high healthcare demand. But it can be difficult to separate the signal from the noise in raw data, which is where the mathematical and statistical models described in this study can help.

"Using these models and the latest disease surveillance data, researchers from New Zealand and Australia provide weekly updates to the Public Health Agency and Te Whatu Ora, as well as public health partners across the Tasman. These contain an analysis of the latest trends and a forecast for the next few weeks.

"Last year saw unusually high levels of influenza through the spring and early summer, due to the novel 'subclade K' of H3N2 influenza. This may be the reason that this winter's flu wave has started later than usual, with low levels through June and July followed by a rapid recent increase. This winter's RSV season has been fairly average in size and timing. Covid-19, which has not yet developed a clear seasonal pattern, has remained at very low levels so far this winter.

"Because of the late start to the influenza season, it is still not too late to get a flu jab if you haven't already had one this year."

Last updated:  14 Aug 2026 12:09pm
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Declared conflicts of interest Professor Michael Plank is lead author of this paper.

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Research The Royal Society, Web page URL after publication
Journal/
conference:
Royal Society Open Science
Research:Paper
Organisation/s: University of Canterbury, The University of Melbourne, James Cook University, The Kids Research Institute Australia, Monash University, Ministry of Health, New Zealand Government
Funder: MJP was supported by a grant from the Marsden Fund (24-UOC-020) and from Te Niwha Infectious Diseases Research Platform, co-hosted by PHF Science and the University of Otago and provisioned by the Ministry of Business, Innovation and Employment, New Zealand (TN/P/24/UoC/MP). OE was supported by a University of Melbourne McKenzie fellowship. FMS was supported by the National Health and Medical Research Council of Australia through the Investigator Grant Scheme (Emerging Leader Fellowship, 2021/GNT2010051). This research is supported by the Australia–Aotearoa Consortium of Epidemic Forecasting and Analytics (ACEFA), a National Health and Medical Research Council of Australia Centre of Research Excellence (2 035 303)
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